Bibliographic record
Abstract
Abstract This paper presents an overview of the principal debates in the literature on noun incorporation, citing key examples and references. There has been much discussion about which constructions can rightly fall under the term ‘noun incorporation’; for example, compounding, denominal, deverbal, light verb, conflation, and narrow scope indefinite constructions have all been treated as noun incorporation constructions. In addition, there has been much discussion about where in the grammar noun incorporation should be handled: the lexicon or the syntax. This debate has shifted with the development of theories without a clear lexicon–syntax division. In the early studies, the main focus was on the morphology of noun incorporation, but in recent years, the focus has shifted to understanding the semantics of the construction, including semantic incorporation, pseudo noun incorporation, detransitivizing, and noun stripping constructions. In addition, there have been many empirical studies over the years exploring subject and modifier incorporation, incorporation of larger phrases and other topics. Noun incorporation studies also intersect with other areas such as bare nominals, complex predicates, possessor raising constructions, and classifier systems. These issues are reviewed in this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".